Upon successful completion of this module, you will be able to:
1. Develop solutions to machine learning problems by modelling and pre-processing data, and designing, selecting and develop appropriate learning algorithms.
2. Consider and contrast the problems of learning and overfitting in an ML system
3. Jutsify the use of linear regression, classification, logistic regression, support vector machines, neural networks, nearest neighbour and clustering.
4. Recommend and construct the use of a machine learning algorithm in unseen situations.

Upon successful completion of this module, you will be able to: 1. Solve a machine learning problem by modelling and pre-processing data, and designing, selecting and implementing appropriate learning algorithms. 2. Explain and contrast the problems of learning and overfitting. 3. Apply machine learning algorithms in unseen situations. 4. Use and explain linear regression, classification, logistic regression, support vector machines, neural networks and reinforcement learning.

Part 1. Components of learning, tasks, types of learning, ML problem formulation,simple predictors
Part 2. Feasibility of learning, error function, Empirical Risk Minimization, generalisationbounds, performance vs complexity, bias/variance trade off, Hoeffding/VC inequalities
Part 3. Feature transformations, noisy data, overfitting, regularisation
Part 4. Logistic regression, gradient descent, Perceptron, Multi Layer Perceptron,Neural Network, backpropagation
Part 5. Hyperplane, separation with hard margin, soft margin, support vector machines,
Part 6. Nearest neighbour classification, linear unsupervised learning, principlecomponent analysis
Part 7. K-means clustering, kernel K-means, advanced clustering algorithms